because of the increasing importance of supplying water for the country, water resource management is of paramount importance. predicting precipitation, as one of the most important climatic parameters, is especially important in using water supplies. time series can be used to predict precipitation. time series analysis seems to be a suitable tool for such forecasting. the present work studies...

[1] We explore implications for modeling and noise analysis of stochastic seasonal processes of climatic origin in geodetic time series. Seasonal signals are generally modeled as sinusoids with annual periods (and harmonics thereof), each with constant amplitude and phase. However, environmental noise that underlies the seasonal signal in geodetic time series has a reddened power spectral densi...

A new approach is proposed for forecasting a time series with multiple seasonal patterns. A state space model is developed for the series using the single source of error approach which enables us to develop explicit models for both additive and multiplicative seasonality. Parameter estimates may be obtained using methods adapted from general exponential smoothing, although the Kalman filter ma...

This paper examines types of cointegration for bivariate seasonal time series, namely seasonal cointegration, periodic cointegration and nonperiodic cointegration. The admissable form(s) for any cointegration is shown to depend crucially on the univariate unit root properties of the series. When both processes are (conventionally) integrated, only nonperiodic cointegration is possible. Periodic...

This paper studies two types of seasonal time series models with periodic variances. Covariance structures of the noise component in the models are discussed. For parameters in the regression component, the performance of the least squares estimates relative to the best linear unbiased estimates is investigated, and some lower bounds for the eecient coeecient deened by the covariance matrices o...

1. Introduction State industry employment is estimated monthly from the Current Employment Statistics survey, a sample of about 380,000 employers, and seasonally adjusted with X-11-ARIMA. An annual benchmarking process revises estimates to reflect universe counts available from administrative records of the Unemployment Insurance (UI) programs of each state. At any point in time, the current se...

This paper introduces the class of seasonal specific structural time series models, according to which each season follows specific dynamics, but is also tied to the others by a common random effects. This results in a dynamic variance components model that can account for some kind of periodic behaviour, such as periodic heteroscedasticity, and is tailored to deal with situations when one or a...

the present research was planned to evaluate the skill of linear stochastic models known as arima and multiplicative seasonal autoregressive integrated moving average (sarima) model in the quantitative forecasting of the standard runoff index (sri) in karkheh basin. to this end, sri was computed in monthly and seasonal time scales in 10 hydrometric stations in 1974-75 to 2012-13 period of time ...

This study develops an improved fuzzy time series models for forecasting short-term series data. The forecasts were obtained by comparing the proposed improved fuzzy time series, Hwang’s fuzzy time series, and heuristic fuzzy time series. The tourism from Taiwan to the United States was used to build the sample sets which were officially published annual data for the period of 1991–2001. The ro...

In this paper, we present a new time series model, which describes self-exciting threshold autoregressive (SETAR) nonlinearity and seasonality simultaneously. The model is termed multiplicative seasonal SETAR (SEASETAR). It can be viewed as a special case of a general non-multiplicative SETAR model by imposing certain restrictions on the parameters of the latter model. Related to these restrict...